Bottom-Up Multi-Person 3D Pose Estimation.
A few bottom-up methods have been proposed for 3D-MPE[1]}, [2]}, [3]}, [4]}. Mehta et al. [1]} first proposed a bottom-up method with an occlusion-robust pose-map (ORPM) to represent the occlusive joints. Such methods can alleviate the occlusion problem to some degree, but they only focus on person-centric and do not infer multiple persons as camera-centric coordinates. While Fabbri et al. [2]} used an encoder-decoder network to compress a heatmap and then decompress it back to the original resolution in the inference time for fast HD image processing. They show superior results when tackling crowded scenes but careless about the occlusion problem. Another method called XNect [3]}, a framework that encodes a 3D location map at the spatial location of each visible joint, which also focuses on a person-centric problem. Zhen et al. [4]} proposed an end-to-end network with the depth-aware part association and bone-length constraints that benefits the 2.5D pose estimation branch of the scheme. However, these models heavily depend on post-processing techniques or refinement. Above bottom-up methods treat camera-centric 3D human pose estimation as two unrelated subtasks: 2.5D pose estimation and camera-centric depth estimation. By contrast, our model focus on exploring the mutual benefits 2.5D pose estimation and camera-centric depths in an end-to-end manner.
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